With the developments of the intelligent transportation technology, the application of license plate detection technology has expanded from original scenes (such as tolls and security checkpoints) having a substantially unchanging background to universal surveillance scenes such as E-polices and doorways. However, such a scene may provide an ever-changing background for a traffic monitoring image obtained therefrom. In such an image, complicated textures and noises may also be present in vicinity of a license plate area. In addition, a strong resemblance between the texture of a non-license plate area (e.g., window, lamp, and radiator of grille of a vehicle, leaves, grass, fences, and road markings) in the image and that of a license plate would lead to a greatly increased error rate in the identification of the license plate area. Meanwhile, a real license plate area may include a part of the background; as a result, the identification of the boundary between the license plate and a part of the background may have a reduced accuracy. Therefore, an improper result and low accuracy of license plate detection may tend to arise from this.